AIChE publishes Coanda paper
Posted on July 27, 2020 Big Data Machine Learning & AI Mathematical Modeling Oilsands Tailings
Our Advanced Instrumentation and Data Science team’s latest white paper has been published by the American Institute of Chemical Engineers .
The paper outlines the development and capabilities of an analysis algorithm developed by Coanda in collaboration with Canadian Natural Resources Limited to estimate the quality of flocculation in Oil Sands tailings slurries.
We present an image analysis algorithm for flocculation quality estimation in high solids
slurries, and demonstrate its performance using inline process images of oil
sands tailings flocculation. While a skilled human operator can often successfully
evaluate such images, variations in feed as well as the lack of isolated flocs or spatial
reference-points inherent in a high-solids slurry can cause conventional image analysis
techniques to fail. We overcome these challenges by recasting the images in Fourier
space, discarding phase information, and applying an eigenfaces-inspired image
recognition algorithm to the resulting spectra. Each image is represented using a few
projection coefficients onto an orthogonal basis and evaluated using likelihood-based
classification schemes. This algorithm shows a high degree of success evaluating the
flocculation quality of 129 batch and inline flocculation experiments (5,610 images
total) utilizing feed tailings from two different oil sand producers at a variety of feed
dilutions and flocculant dosing levels.
eigenfaces, flocculation, image recognition, mixing, online measurement
The full paper can be accessed on the AIChE website here (subscription required):
Eigenspectra for Flocculation Quality Estimation
Explore our Oil Sands, Tailings and Mathematical Modelling pages for more information on Coanda’s capabilities in these areas.